
'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase					:    Tuple           = logging.get_logger(__name__)







def 		A_(     A						:		Dict):



							if "resnet-50" in model_name:
														UpperCamelCase   			=     ResNetConfig.from_pretrained('microsoft/resnet-50')
							elif "resnet-101" in model_name:
														UpperCamelCase   			=     ResNetConfig.from_pretrained('microsoft/resnet-101')
							else:
														raise ValueError('Model name should include either resnet50 or resnet101')

							UpperCamelCase   			=     DetrConfig(use_timm_backbone=a__		,     backbone_config=a__)

							# set label attributes
							UpperCamelCase   			=     """panoptic""" in model_name
							if is_panoptic:
														UpperCamelCase   			=     250
							else:
														UpperCamelCase   			=     91
														UpperCamelCase   			=     """huggingface/label-files"""
														UpperCamelCase   			=     """coco-detection-id2label.json"""
														UpperCamelCase   			=     json.load(open(hf_hub_download(a__		,     a__		,     repo_type='dataset')		,     'r'))
														UpperCamelCase   			=     {int(a__): v for k, v in idalabel.items()}
														UpperCamelCase   			=     idalabel
														UpperCamelCase   			=     {v: k for k, v in idalabel.items()}

							return config, is_panoptic







def 		A_(     A						:		List[Any]):
							UpperCamelCase   			=     []

							# stem
							# fmt: off
							rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight'))
							rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight'))
							rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias'))
							rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'))
							rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var'))
							# stages
							for stage_idx in range(len(config.backbone_config.depths)):
														for layer_idx in range(config.backbone_config.depths[stage_idx]):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																												    ))
																					# 3 convs
																					for i in range(3):
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																												    ))
																												rename_keys.append(
																												    (
																												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																												    ))
    # fmt: on

							for i in range(config.encoder_layers):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
														        f'''encoder.layers.{i}.self_attn.out_proj.weight''',
														    ))
														rename_keys.append(
														    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias'''))
														rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
														rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
														rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
														rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
														rename_keys.append(
														    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight'''))
														rename_keys.append(
														    (f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
														rename_keys.append(
														    (f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
														rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
														        f'''decoder.layers.{i}.self_attn.out_proj.weight''',
														    ))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias'''))
														rename_keys.append(
														    (
														        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
														        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
														    ))
														rename_keys.append(
														    (
														        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
														        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
														    ))
														rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
														rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
														rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
														rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight'''))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight'''))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias'''))
														rename_keys.append(
														    (f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
														rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('input_proj.weight', 'input_projection.weight'),
							        ('input_proj.bias', 'input_projection.bias'),
							        ('query_embed.weight', 'query_position_embeddings.weight'),
							        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
							        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
							        ('class_embed.weight', 'class_labels_classifier.weight'),
							        ('class_embed.bias', 'class_labels_classifier.bias'),
							        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
							        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
							        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
							        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
							        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
							        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
							    ])

							return rename_keys







def 		A_(     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		Optional[Any]):
							UpperCamelCase   			=     state_dict.pop(a__)
							UpperCamelCase   			=     val







def 		A_(     A						:		str		,     A						:		int=False):
							UpperCamelCase   			=     """"""
							if is_panoptic:
														UpperCamelCase   			=     """detr."""

							# first: transformer encoder
							for i in range(6):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														UpperCamelCase   			=     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
														UpperCamelCase   			=     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase   			=     in_proj_weight[:256, :]
														UpperCamelCase   			=     in_proj_bias[:256]
														UpperCamelCase   			=     in_proj_weight[256:512, :]
														UpperCamelCase   			=     in_proj_bias[256:512]
														UpperCamelCase   			=     in_proj_weight[-256:, :]
														UpperCamelCase   			=     in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6):
														# read in weights + bias of input projection layer of self-attention
														UpperCamelCase   			=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
														UpperCamelCase   			=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase   			=     in_proj_weight[:256, :]
														UpperCamelCase   			=     in_proj_bias[:256]
														UpperCamelCase   			=     in_proj_weight[256:512, :]
														UpperCamelCase   			=     in_proj_bias[256:512]
														UpperCamelCase   			=     in_proj_weight[-256:, :]
														UpperCamelCase   			=     in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														UpperCamelCase   			=     state_dict.pop(
														    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
														UpperCamelCase   			=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														UpperCamelCase   			=     in_proj_weight_cross_attn[:256, :]
														UpperCamelCase   			=     in_proj_bias_cross_attn[:256]
														UpperCamelCase   			=     in_proj_weight_cross_attn[256:512, :]
														UpperCamelCase   			=     in_proj_bias_cross_attn[256:512]
														UpperCamelCase   			=     in_proj_weight_cross_attn[-256:, :]
														UpperCamelCase   			=     in_proj_bias_cross_attn[-256:]







def 		A_(     ):
							UpperCamelCase   			=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCamelCase   			=     Image.open(requests.get(a__		,     stream=a__).raw)

							return im







@torch.no_grad()
def 		A_(     A						:		int		,     A						:		int=None		,     A						:		Union[str, Any]=False):
							UpperCamelCase   			=     get_detr_config(a__)

							# load original model from torch hub
							UpperCamelCase   			=     {
							    """detr-resnet-50""": """detr_resnet50""",
							    """detr-resnet-101""": """detr_resnet101""",
							}
							logger.info(f'''Converting model {model_name}...''')
							UpperCamelCase   			=     torch.hub.load('facebookresearch/detr'		,     model_name_to_original_name[model_name]		,     pretrained=a__).eval()
							UpperCamelCase   			=     detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(a__):
														if is_panoptic:
																					UpperCamelCase   			=     """detr.""" + src
														rename_key(a__		,     a__		,     a__)
							# query, key and value matrices need special treatment
							read_in_q_k_v(a__		,     is_panoptic=a__)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCamelCase   			=     """detr.model.""" if is_panoptic else """model."""
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('detr')
																					    and not key.startswith('class_labels_classifier')
																					    and not key.startswith('bbox_predictor')
																					):
																												UpperCamelCase   			=     state_dict.pop(a__)
																												UpperCamelCase   			=     val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCamelCase   			=     state_dict.pop(a__)
																												UpperCamelCase   			=     val
																					elif key.startswith('bbox_attention') or key.startswith('mask_head'):
																												continue
																					else:
																												UpperCamelCase   			=     state_dict.pop(a__)
																												UpperCamelCase   			=     val
														else:
																					if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
																												UpperCamelCase   			=     state_dict.pop(a__)
																												UpperCamelCase   			=     val

    # finally, create HuggingFace model and load state dict
							UpperCamelCase   			=     DetrForSegmentation(a__) if is_panoptic else DetrForObjectDetection(a__)
							model.load_state_dict(a__)
							model.eval()

							# verify our conversion on an image
							UpperCamelCase   			=     """coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCamelCase   			=     DetrImageProcessor(format=a__)

							UpperCamelCase   			=     processor(images=prepare_img()		,     return_tensors='pt')
							UpperCamelCase   			=     encoding["""pixel_values"""]

							UpperCamelCase   			=     detr(a__)
							UpperCamelCase   			=     model(a__)

							assert torch.allclose(outputs.logits		,     original_outputs['pred_logits']		,     atol=1E-3)
							assert torch.allclose(outputs.pred_boxes		,     original_outputs['pred_boxes']		,     atol=1E-3)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks		,     original_outputs['pred_masks']		,     atol=1E-4)
							print('Looks ok!')

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
														Path(a__).mkdir(exist_ok=a__)
														model.save_pretrained(a__)
														processor.save_pretrained(a__)

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('Uploading PyTorch model and image processor to the hub...')
														model.push_to_hub(f'''nielsr/{model_name}''')
														processor.push_to_hub(f'''nielsr/{model_name}''')


if __name__ == "__main__":
						lowerCAmelCase					:    List[Any]           = argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name',
						    default='detr-resnet-50',
						    type=str,
						    choices=['detr-resnet-50', 'detr-resnet-101'],
						    help='Name of the DETR model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
						)
						parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
						lowerCAmelCase					:    str           = parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


